An enhanced segmentation of blood vessels in retinal images using contourlet

• Published in: 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Vol. 2008; pp. 3530 - 3533
• Main Authors: Rezatofighi, S. H., Roodaki, A., Ahmadi Noubari, H.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.01.2008
• Subjects: Algorithms; Biomedical imaging; Blood vessels; Contrast Media - pharmacology; Feature extraction; Fuzzy Logic; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image Processing, Computer-Assisted - methods; Image segmentation; Neurons; Observer Variation; Pattern Recognition, Automated - methods; Reproducibility of Results; Retina; Retina - pathology; Retinal Vessels - pathology; Retinoscopy - methods; Sensitivity and Specificity; Software; Transforms
• ISBN: 9781424418145; 1424418143
• ISSN: 1094-687X; 1557-170X; 1558-4615
• DOI: 10.1109/IEMBS.2008.4649967

Retinal images acquired using a fundus camera often contain low grey, low level contrast and are of low dynamic range. This may seriously affect the automatic segmentation stage and subsequent results; hence, it is necessary to carry-out preprocessing to improve image contrast results before segmentation. Here we present a new multi-scale method for retinal image contrast enhancement using Contourlet transform. In this paper, a combination of feature extraction approach which utilizes Local Binary Pattern (LBP), morphological method and spatial image processing is proposed for segmenting the retinal blood vessels in optic fundus images. Furthermore, performance of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multilayer Perceptron (MLP) is investigated in the classification section. The performance of the proposed algorithm is tested on the publicly available DRIVE database. The results are numerically assessed for different proposed algorithms.